Instructions to use JunHwi/kmhas_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHwi/kmhas_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHwi/kmhas_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHwi/kmhas_binary") model = AutoModelForSequenceClassification.from_pretrained("JunHwi/kmhas_binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67060f5f4c210870bf34ea5f1f287b243eabe55bf63b2d3e55f2368208f685d8
- Size of remote file:
- 452 MB
- SHA256:
- 32aa26851c1e8317293abebb33ee2f2eda797dd53ac85872431559ed8196c3bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.